Multimodal medical image fusion based on content-based decomposition and PCA-Sigmoid

2021 ◽  
Vol 17 ◽  
Author(s):  
Srinivasu Polinati ◽  
Durga Prasad Bavirisetti ◽  
Kandala N V P S Rajesh ◽  
Ravindra Dhuli
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Single Photon ◽  
Photon Emission ◽  
Emission Computed Tomography ◽  
Sigmoid Function ◽  
Fusion Algorithm ◽  
Medical Image Fusion ◽  
Empirical Wavelet Transform ◽  
Weight Scaling

Objective: The objective of any multimodal medical image fusion algorithm is to assist a radiologist for better decision-making during the diagnosis and therapy by integrating the anatomical (magnetic resonance imaging) and functional (positron emission tomography/single-photon emission computed tomography) information. Methods: We proposed a new medical image fusion method based on content-based decomposition, principal component analysis (PCA), and sigmoid function. We considered empirical wavelet transform (EWT) for content-based decomposition purposes since it can preserve crucial medical image information such as edges and corners. PCA is used to obtain initial weights corresponding to each detail layer. Results: In our experiments, we found that direct usage of PCA for detail layer fusion introduces severe artifacts into the fused image due to weight scaling issues. In order to tackle this, we considered using the sigmoid function for better weight scaling. We considered 24 pairs of MRI-PET and 24 pairs of MRI-SPECT images for fusion and the results are measured using four significant quantitative metrics. Conclusion: Finally, we compared our proposed method with other state-of-the-art transform-based fusion approaches, using traditional and recent performance measures. An appreciable improvement is observed in both qualitative and quantitative results compared to other fusion methods.

scholarly journals Multimodal Medical Image Fusion Using Various Hybrid Fusion Techniques For clinical Treatment Analysis

2018 ◽  
Vol 2 (3) ◽  
Author(s):  
Rajalingam B ◽  
Priya R
Keyword(s):  
Computed Tomography ◽  
Image Fusion ◽  
Medical Image ◽  
Single Photon ◽  
Photon Emission ◽  
Experimental Results ◽  
Emission Computed Tomography ◽  
Medical Image Fusion ◽  
Advantages And Disadvantages ◽  
Hybrid Fusion

Medical image fusion is one the most significant and useful disease analytic techniques. This research paper proposed and examines some of the hybrid multimodality medical image fusion methods and discusses the most essential advantages and disadvantages of these methods to develop hybrid multimodal image fusion algorithms that improve the feature of merged multimodality therapeutic image. Computed Tomography, Magnetic Resonance Imaging, Positron Emission Tomography and Single Photon Emission Computed Tomography are the input multimodal therapeutic images used for fusion process. An experimental results of proposed all hybrid fusion techniques provides the best fused multimodal medical images of highest quality, highest details, shortest processing time, and best visualization. Both traditional and hybrid multimodal medical image fusion algorithms are evaluated using several quality metrics. Compared with other existing techniques the proposed technique experimental results demonstrate the better processing performance and results in both subjective and objective evaluation criteria. This is favorable, especially for helping in accurate clinical disease analysis.

Hybrid Multimodality Medical Image Fusion Using Various Fusion Techniques With Quantitative and Qualitative Analysis

2019 ◽  
pp. 206-233 ◽  
Author(s):  
Rajalingam B. ◽  
Priya R. ◽  
Bhavani R.
Keyword(s):  
Computed Tomography ◽  
Image Fusion ◽  
Medical Image ◽  
Single Photon ◽  
Photon Emission ◽  
Fusion Algorithm ◽  
Medical Image Fusion ◽  
Fused Image ◽  
Visual Description ◽  
The Individual

In this chapter, different types of image fusion techniques have been studied and evaluated in the medical applications. The ultimate goal of this proposed method is to obtain the fused image without any loss of similar information and preserve all special features present in the input medical images. This method is used to improve the fused image quality for better diagnosis of critical disease analysis. The fused hybrid multimodal medical image should convey better visual description than the individual input images. This chapter proposes the method for multimodal medical image fusion using the hybrid fusion algorithm. The computed tomography, magnetic resonance imaging, positron emission tomography, and single photon emission computed tomography are the input images used for this experimental work. In this chapter, experimental results discovered that the proposed techniques provide better visualization of fused image and gives the superior results compared to various existing traditional algorithms.

Enhancement of Hybrid Multimodal Medical Image Fusion Techniques for Clinical Disease Analysis

2018 ◽  
Vol 8 (3) ◽  
pp. 16-40 ◽  
Author(s):  
Rajalingam B. ◽  
Priya R.
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Single Photon ◽  
Photon Emission ◽  
Clinical Disease ◽  
Experimental Result ◽  
Emission Computed Tomography ◽  
Medical Image Fusion ◽  
Advantages And Disadvantages ◽  
Disease Analysis

Multimodal medical image fusion is one the most significant and useful disease analytic techniques. This research article proposes the hybrid multimodality medical image fusion methods and discusses the most essential advantages and disadvantages of these methods. The hybrid multimodal medical image fusion algorithms are used to improve the quality of fused multimodality medical image. Magnetic resonance imaging, positron emission tomography, and single photon emission computed tomography are the input multimodal therapeutic images used for fusion process. An experimental result of proposed hybrid fusion techniques provides the fused multimodal medical images of highest quality, shortest processing time, and best visualization. Both traditional and hybrid multimodal medical image fusion algorithms are evaluated using several quality metrics. Compared with existing techniques the proposed result gives the better processing performance in both qualitative and quantitative evaluation criteria. This is favorable, especially for helping in accurate clinical disease analysis.

scholarly journals Multimodal Medical Image Fusion Using Various Hybrid Fusion Techniques For clinical Treatment Analysis

2018 ◽  
Author(s):  
Rajalingam B ◽  
Priya R
Keyword(s):  
Computed Tomography ◽  
Image Fusion ◽  
Medical Image ◽  
Single Photon ◽  
Photon Emission ◽  
Experimental Results ◽  
Emission Computed Tomography ◽  
Medical Image Fusion ◽  
Advantages And Disadvantages ◽  
Hybrid Fusion

Medical image fusion is one the most significant and useful disease analytic techniques. This research paper proposed and examines some of the hybrid multimodality medical image fusion methods and discusses the most essential advantages and disadvantages of these methods to develop hybrid multimodal image fusion algorithms that improve the feature of merged multimodality therapeutic image. Computed Tomography, Magnetic Resonance Imaging, Positron Emission Tomography and Single Photon Emission Computed Tomography are the input multimodal therapeutic images used for fusion process. An experimental results of proposed all hybrid fusion techniques provides the best fused multimodal medical images of highest quality, highest details, shortest processing time, and best visualization. Both traditional and hybrid multimodal medical image fusion algorithms are evaluated using several quality metrics. Compared with other existing techniques the proposed technique experimental results demonstrate the better processing performance and results in both subjective and objective evaluation criteria. This is favorable, especially for helping in accurate clinical disease analysis.

Medical Image Fusion in Wavelet and Ridgelet Domains

2017 ◽  
pp. 711-723
Author(s):  
Vikrant Bhateja ◽  
Abhinav Krishn ◽  
Himanshi Patel ◽  
Akanksha Sahu
Keyword(s):  
Image Fusion ◽  
Information Content ◽  
Medical Image ◽  
Similarity Index ◽  
Principal Component ◽  
Structural Similarity ◽  
Fusion Algorithm ◽  
Medical Image Fusion ◽  
Multi Scale ◽  
Fused Image

Medical image fusion facilitates the retrieval of complementary information from medical images and has been employed diversely for computer-aided diagnosis of life threatening diseases. Fusion has been performed using various approaches such as Pyramidal, Multi-resolution, multi-scale etc. Each and every approach of fusion depicts only a particular feature (i.e. the information content or the structural properties of an image). Therefore, this paper presents a comparative analysis and evaluation of multi-modal medical image fusion methodologies employing wavelet as a multi-resolution approach and ridgelet as a multi-scale approach. The current work tends to highlight upon the utility of these approaches according to the requirement of features in the fused image. Principal Component Analysis (PCA) based fusion algorithm has been employed in both ridgelet and wavelet domains for purpose of minimisation of redundancies. Simulations have been performed for different sets of MR and CT-scan images taken from ‘The Whole Brain Atlas'. The performance evaluation has been carried out using different parameters of image quality evaluation like: Entropy (E), Fusion Factor (FF), Structural Similarity Index (SSIM) and Edge Strength (QFAB). The outcome of this analysis highlights the trade-off between the retrieval of information content and the morphological details in finally fused image in wavelet and ridgelet domains.

scholarly journals Local energy match based non-sub sampled contourlet Transform for multi modal medical image fusion

2018 ◽  
Vol 7 (2.31) ◽  
pp. 165
Author(s):  
M Shyamala Devi ◽  
P Balamurugan
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Gabor Filter ◽  
Low Frequency ◽  
Local Energy ◽  
Fusion Algorithm ◽  
Medical Image Fusion ◽  
Fused Image ◽  
Image Averaging

Image processing technology requires moreover the full image or the part of image which is to be processed from the user’s point of view like the radius of object etc. The main purpose of fusion is to diminish dissimilar error between the fused image and the input images. With respect to the medical diagnosis, the edges and outlines of the concerned objects is more important than extra information. So preserving the edge features of the image is worth for investigating the image fusion. The image with higher contrast contains more edge-like features. Here we propose a new medical image fusion scheme namely Local Energy Match NSCT based on discrete contourlet transformation, which is constructive to give the details of curve edges. It is used to progress the edge information of fused image by dropping the distortion. This transformation lead to crumbling of multimodal image addicted to finer and coarser details and finest details will be decayed into unusual resolution in dissimilar orientation. The input multimodal images namely CT and MRI images are first transformed by Non Sub sampled Contourlet Transformation (NSCT) which decomposes the image into low frequency and high frequency elements. In our system, the Low frequency coefficient of the image is fused by image averaging and Gabor filter bank algorithm. The processed High frequency coefficients of the image are fused by image averaging and gradient based fusion algorithm. Then the fused image is obtained by inverse NSCT with local energy match based coefficients. To evaluate the image fusion accuracy, Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Correlation Coefficient parameters are used in this work .

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